Advanced In Silico Drug Design Training Course

Advanced In Silico Drug Design Training Course

Introduction

Advanced In Silico Drug Design Training Course provides an intensive exploration into the application of computational methods and algorithms to accelerate the drug discovery process. This field, often called Computer-Aided Drug Design (CADD), utilizes powerful software and bioinformatics to model and simulate molecular interactions, predict drug properties, and identify promising drug candidates without extensive and costly laboratory experiments. This course will cover cutting-edge techniques, including advanced molecular modeling, high-throughput virtual screening, and the application of artificial intelligence (AI) and machine learning (ML) in drug development. Participants will gain practical, hands-on experience with industry-standard tools and platforms, enabling them to become proficient practitioners capable of revolutionizing pharmaceutical research and development.

The training is meticulously designed to bridge the gap between theoretical knowledge and practical application, focusing on real-world challenges in modern drug discovery. Key topics include structure-based drug design (SBDD), ligand-based drug design (LBDD), pharmacokinetics (PK), and pharmacodynamics (PD). You'll learn how to navigate vast chemical and biological databases, predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and optimize lead compounds for improved efficacy and safety. The curriculum is enriched with practical case studies from various therapeutic areas, such as oncology and infectious diseases, ensuring that participants can apply their skills to solve complex problems and contribute to the development of next-generation therapeutics. This course is your pathway to a high-impact career in the dynamic and evolving field of computational drug discovery.

Course Duration

10 days

Course Objectives

1. Master the principles of computer-aided drug design (CADD) and its role in modern drug discovery.
2. Apply advanced molecular docking and virtual screening techniques to identify novel lead compounds.
3. Utilize structure-based drug design (SBDD) and ligand-based drug design (LBDD) for lead optimization.
4. Analyze and predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties using computational models.
5. Implement molecular dynamics simulations to study protein-ligand binding kinetics and stability.
6. Leverage machine learning (ML) and artificial intelligence (AI) for predictive modeling and drug candidate prioritization.
7. Conduct pharmacophore modeling and Quantitative Structure-Activity Relationship (QSAR) studies.
8. Design and perform high-throughput screening (HTS) campaigns using computational tools.
9. Develop custom workflows for de novo drug design and scaffold hopping.
10. Interpret complex bioinformatics and structural biology data for target validation.
11. Evaluate and select appropriate computational tools and databases for specific research projects.
12. Integrate multi-omics data (genomics, proteomics) into the drug discovery pipeline.
13. Contribute to multidisciplinary research teams in pharmaceutical and biotechnology industries.

Organizational Benefits

• Significantly reduces the time and cost associated with identifying and validating drug candidates.
• Early prediction of ADMET properties and potential toxicities leads to a lower rate of failure in clinical trials.
• Enables the exploration of novel chemical spaces and the design of breakthrough therapeutics.
• Optimizes resource allocation by prioritizing the most promising compounds for experimental validation.
• Equips your team with the latest skills and technologies to stay at the forefront of the pharmaceutical industry.
• Fosters a culture of data-driven research, leading to more informed and strategic decisions in drug development.

Target Audience

• Researchers and Scientists in pharmaceutical and biotech companies.
• Graduate and Postgraduate Students (M.Sc., Ph.D.) in bioinformatics, computational chemistry, medicinal chemistry, and pharmacology.
• Academicians and Professors involved in drug discovery research.
• Professionals looking to transition into a career in computational biology or cheminformatics.
• Data Scientists and Machine Learning Engineers interested in applying their skills to the healthcare sector.
• Bioinformaticians and Computational Biologists aiming to specialize in drug design.
• Medical and Pharmacy Students seeking to understand the role of technology in modern medicine.
• R&D Managers needing to understand and implement in silico strategies in their pipelines.

Course Modules

Module 1: Foundations of In Silico Drug Discovery

• Introduction to the drug discovery pipeline and the role of CADD.
• Principles of molecular mechanics, quantum mechanics, and force fields.
• Navigating key databases: PDB, PubChem, ZINC, and ChEMBL.
• Case Study: Identifying a potential target for a specific disease using bioinformatics databases.
• Introduction to molecular visualization software like PyMOL and UCSF Chimera.

Module 2: Structure-Based Drug Design (SBDD)

• Protein and ligand preparation for docking.
• Principles of molecular docking and scoring functions.
• Advanced docking algorithms and flexible docking.
• Case Study: Designing a potent inhibitor for a well-known enzyme, such as HIV-1 protease, based on its crystal structure.
• Analyzing protein-ligand interactions: H-bonds, hydrophobic interactions, etc.

Module 3: Ligand-Based Drug Design (LBDD)

• Pharmacophore modeling and its application in drug discovery.
• 2D and 3D QSAR (Quantitative Structure-Activity Relationship) analysis.
• Chemical similarity and virtual screening using molecular fingerprints.
• Case Study: Developing a QSAR model to predict the activity of a series of kinase inhibitors.
• Scaffold hopping and lead optimization using LBDD principles.

Module 4: Virtual High-Throughput Screening (vHTS)

• Strategies for building and screening large compound libraries.
• Parallelization and optimization of virtual screening campaigns.
• Filtering and ranking hits from vHTS results.
• Case Study: Screening a million-compound library to identify novel hits for an oncology target.
• Combining SBDD and LBDD approaches for more effective screening.

Module 5: Predicting ADMET Properties

• Introduction to computational ADMET prediction.
• In silico models for absorption, distribution, and metabolism prediction.
• Toxicity prediction and safety pharmacology.
• Case Study: Predicting the metabolic fate and potential toxicity of a newly designed lead compound.
• Using multi-parameter optimization (MPO) to balance efficacy and ADMET properties.

Module 6: Advanced Molecular Dynamics (MD) Simulations

• Setting up and running MD simulations of protein-ligand complexes.
• Analyzing MD trajectories to understand protein-ligand stability and dynamics.
• Binding free energy calculations (e.g., MM/PBSA).
• Case Study: Simulating the binding of an antagonist to a G-protein coupled receptor (GPCR) to understand its mechanism of action.
• Steered Molecular Dynamics (SMD) for drug unbinding simulations.

Module 7: AI and Machine Learning in Drug Discovery

• Introduction to machine learning algorithms for drug discovery.
• Deep learning models for target prediction and molecular generation.
• Graph-based neural networks for molecular property prediction.
• Case Study: Building a deep learning model to predict the binding affinity of small molecules to a specific protein target.
• Generative AI for designing novel molecules from scratch.

Module 8: De Novo Drug Design and Fragment-Based Drug Discovery (FBDD)

• Principles of de novo drug design and scaffold generation.
• Computational methods for FBDD.
• Merging and growing fragments to create high-affinity binders.
• Case Study: Using a computational approach to design a novel compound targeting a protein-protein interaction (PPI).
• Structure-based fragment screening and hit validation.

Module 9: Pharmacokinetics & Pharmacodynamics (PK/PD) Modeling

• Introduction to computational PK/PD modeling.
• Predicting drug concentration and effect over time.
• Translational modeling from preclinical to clinical stages.
• Case Study: Building a PK/PD model to predict the dosing regimen for a new drug.
• Systems pharmacology and network-based drug discovery.

Module 10: Integrating Multi-Omics Data

• Using genomics and proteomics data for target identification.
• Network biology and pathway analysis in drug discovery.
• Integrating clinical and phenotypic data.
• Case Study: Identifying new drug targets for cancer by integrating transcriptomics and proteomics data.
• Utilizing single-cell and spatial omics for precision medicine.

Module 11: Real-World Drug Discovery Workflows

• Designing a complete in silico drug discovery pipeline.
• Best practices for project management and data handling.
• Strategies for hit-to-lead and lead optimization.
• Case Study: A full-cycle project from target identification to lead optimization for a neglected tropical disease.
• Discussion on collaboration between computational and experimental teams.

Module 12: Advanced Topics and Emerging Trends

• Drug repurposing and repositioning strategies.
• Targeting difficult proteins like intrinsically disordered proteins (IDPs).
• Covalent inhibitors and allosteric modulators.
• Case Study: Identifying a known drug that could be repurposed for a new indication using computational methods.
• Quantum computing and its potential impact on drug design.

Module 13: Computational Toxicology and Safety

• In silico models for predicting toxicity, carcinogenicity, and mutagenicity.
• Regulatory guidelines for using computational data (e.g., ICH M7).
• Case Study: Assessing the genotoxicity of a compound using computational toxicology models.
• Integrating in vitro and in vivo data with in silico predictions.

Module 14: Protein Engineering and Biologics Design

• Introduction to computational methods for biologics (antibodies, peptides).
• Protein-protein docking and interface analysis.
• Designing stable and effective therapeutic proteins.
• Case Study: Optimizing an antibody's binding affinity and stability using computational design.
• Introduction to PROTACs and other protein degradation technologies.

Module 15: Final Project and Presentation

• Participants work on an individual or team-based project from a list of predefined or self-proposed topics.
• Present their methodologies, results, and conclusions to the class.
• Receive feedback from instructors and peers.
• Case Study: An open-ended project where students apply all learned skills to address a contemporary drug discovery challenge, from target to final lead compound.
• Report writing and scientific communication best practices.

Training Methodology

• Instructor-Led Sessions
• Hands-on Labs
• Real-World Case Studies
• Group Discussions & Problem-Solving: 
• Live Demonstrations.
• Q&A Sessions
• Project-Based Learning.

Register as a group from 3 participants for a Discount

Send us an email: [email protected] or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.